Experimentation and Approval Mechanisms

Abstract

We study the design of approval rules when costly experimentation must be delegated to an agent with misaligned preferences. When the agent has the option to end experimentation, we show that in contrast to standard stopping problems, the optimal approval rule must be history‐dependent. We characterize the optimal rule and show the approval threshold moves downward over the course of experimentation. We find that private information may qualitatively change the optimal mechanism: an agent can choose a fast‐track option in the form of an initially depressed approval threshold. On expiry of the fast track, the threshold jumps up, introducing more stringent standards. Our results provide a theoretical foundation for both the loosening of approval standards on longer experimentation paths and fast‐track mechanisms.